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自适应多模态特征融合胶质瘤分级网络
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作者：王黎,曹颖,田梨梨,陈祈剑,郭顺超,张健,王丽会(贵州大学计算机科学与技术学院, 贵州省智能医学影像分析与精准诊断重点实验室, 贵阳 550025) 摘要：Objective Glioma grading has been a vital research tool for customized treatment in of the glioma. Glioma grading can be an assessment tool for biopsy and histopathological to resolve invasive and time-consuming issues. A non-invasive scheme for grading gliomas precision has played the key role. A reliable non-invasive grading scheme has been implemented for magnetic resonance imaging (MRI)to facilitate computer-assisted diagnosis system(CAD) for glioma grading. The medical image-based grading has been used manual features to implement image-level tumor analysis. Manual feature-based methods have realized higher area under curve (AUC) based on the variation of image intensity and image deformation analyses constrained generalization capability. The emerging deep learning method has projected deep features more semantic and representative compared with the manual features in generalization.......... |
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